Results for "deep nets difficulty"

Assigning labels per pixel (semantic) or per instance (instance segmentation) to map object boundaries.

Variability introduced by minibatch sampling during SGD.

AI subfield dealing with understanding and generating human language, including syntax, semantics, and pragmatics.

The shape of the loss function over parameter space.

Converting audio speech into text, often using encoder-decoder or transducer architectures.

A narrow minimum often associated with poorer generalization.

Generating speech audio from text, with control over prosody, speaker identity, and style.

A wide basin often correlated with better generalization.

Gradually increasing learning rate at training start to avoid divergence.

Empirical laws linking model size, data, compute to performance.

Strategy mapping states to actions.

Combines value estimation (critic) with policy learning (actor).

Expected return of taking action in a state.

Embedding signals to prove model ownership.

Neural networks that operate on graph-structured data by propagating information along edges.

Probabilistic energy-based neural network with hidden variables.

Extension of convolution to graph domains using adjacency structure.

Combining signals from multiple modalities.

GNN using attention to weight neighbor contributions dynamically.

Generating human-like speech from text.

Changing speaker characteristics while preserving content.

Maps audio signals to linguistic units.

Identifying speakers in audio.

Generates audio waveforms from spectrograms.

Increasing model capacity via compute.

Visualization of optimization landscape.

Flat high-dimensional regions slowing training.

AI systems that perceive and act in the physical world through sensors and actuators.

Methods like Adam adjusting learning rates dynamically.

Human-like understanding of physical behavior.